The development of new approaches to detect motor-related brain activity is key in many aspects of science, especially in brain–computer interface applications. Even though some well-known features of motor-related electroencephalograms have been revealed using traditionally applied methods, they still lack a robust classification of motor-related patterns. Here, we introduce new features of motor-related brain activity and uncover hidden mechanisms of the underlying neuronal dynamics by considering event-related desynchronization (ERD) of $\mu$ -rhythm in the sensorimotor cortex, i.e., tracking the decrease of the power spectral density in the corresponding frequency band. We hypothesize that motor-related ERD is associated with the suppression of random fluctuations of $\mu$ -band neuronal activity. This is due to the lowering of the number of active neuronal populations involved in the corresponding oscillation mode. In this case, we expect more regular dynamics and a decrease in complexity of the EEG signal recorded over the sensorimotor cortex. In order to support this, we apply measures of signal complexity by means of recurrence quantification analysis (RQA). In particular, we demonstrate that certain RQA quantifiers are very useful to detect the moment of movement onset and, therefore, are able to classify the laterality of executed movements.

Many approaches to automated epileptic seizure detection share a common challenge — the trade-off between recall and precision. This study aims to develop a novel approach for reducing false positive predictions in seizure detection tasks applied to real-world EEG recordings. We propose a multi-stage modeling framework, for which the novelty lies in combination of traditional machine learning outlier detection with state-of-the-art convolutional neural networks. Our dataset includes raw epileptic EEG data directly from the hospital. Continuous wavelet analysis is employed for EEG preprocessing and feature extraction. We evaluated the performance of the proposed two-stage algorithm, and it demonstrated a slight decrease in recall but a significant improvement in precision in comparison to machine-learning-only or neural-network-only algorithms. We hypothesize that this finding aligns well with our previous research and relates to the fundamental properties of epileptic EEG, including the extreme behavior of seizures. Finally, we propose a potential practical application of the developed approach within a clinical decision support system.

Sensor-level human brain activity is studied during real and imaginary motor execution using functional near-infrared spectroscopy (fNIRS). Blood oxygenation and deoxygenation spatial dynamics exhibit pronounced hemispheric lateralization when performing motor tasks with the left and right hands. This fact allowed us to reveal biomarkers of hemodynamical response of the motor cortex on the motor execution, and use them for designing a sensing method for classification of the type of movement. The recognition accuracy of real movements is close to 100%, while the classification accuracy of imaginary movements is lower but quite high (at the level of 90%). The advantage of the proposed method is its ability to classify real and imaginary movements with sufficiently high efficiency without the need for recalculating parameters. The proposed system can serve as a sensor of motor activity to be used for neurorehabilitation after severe brain injuries, including traumas and strokes.

Neuroeducation seeks to implement knowledge about neural mechanisms of learning into educational practice and to understand the impact of learning itself. The crucial tasks in this field are to evaluate and to enhance cognitive abilities, that are used in monitoring educational performance, but also known to greatly impact learning process. Contemporary neuroscience achieved significant progress in measuring brain cognitive abilities through mental state assessment. Popular approach to this task based on brain-computer interface can be difficult to implement in the context of education, but general concept of neuroadaptation is still plausible. In this study, we propose open-loop neuroadaptive system for enhancing student’s cognitive abilities in learning. Assessment of cognitive abilities is based on the concept of executive functions. We design EEG study with special tests and use combined analysis of behavioral and brain activity to assess the level of development of cognitive abilities. Feedback in this system is implemented in the form of recommendations aimed to develop and enhance underdeveloped cognitive abilities and skills. Recommendations have form of various types of extracurricular activities and are based on extensive literature search. This is the system with open-loop adaptation, as it can assess cognitive abilities, provide feedback aimed to enhance these abilities and then after a period of time it can assess cognitive abilities again as a part of the next loop. We believe that developed neuroadaptive system has a potential to be used in educational institutes.

In this paper, we used an EEG system to monitor and analyze the cortical activity of children and adults at a sensor level during cognitive tasks in the form of a Schulte table. This complex cognitive task simultaneously involves several cognitive processes and systems: visual search, working memory, and mental arithmetic. We revealed that adults found numbers on average two times faster than children in the beginning. However, this difference diminished at the end of table completion to 1.8 times. In children, the EEG analysis revealed high parietal alpha-band power at the end of the task. This indicates the shift from procedural strategy to less demanding fact-retrieval. In adults, the frontal beta-band power increased at the end of the task. It reflects enhanced reliance on the top–down mechanisms, cognitive control, or attentional modulation rather than a change in arithmetic strategy. Finally, the alpha-band power of adults exceeded one of the children in the left hemisphere, providing potential evidence for the fact-retrieval strategy. Since the completion of the Schulte table involves a whole set of elementary cognitive functions, the obtained results were essential for developing passive brain–computer interfaces for monitoring and adjusting a human state in the process of learning and solving cognitive tasks of various types.